NO/NOS‐Dependent Modulation of Inflammation in Acrolein‐Induced Vascular Toxicity
Bibliographic record
Abstract
Modulation of inflammatory signaling has been elucidated in different disease models including vascular biology. No/NOS system plays an important role in the process of inflammation. Balance between nitric oxide (NO) and superoxide is critical in inflammatory process. Peroxisome proliferator‐activated receptor gamma (PPARγ) has been implicated in pathology of diseases involving inflammation and in our previous studies we reported reduction in PPARγ protein expression and activity in animals exposed to acrolein. Since PPARγ influences both NO and superoxide generation, in this study we are proposing involvement of impairment of this regulation in acrolein‐mediated inflammatory response. Male iNOS knockout (inducible nitric oxide synthase, KO) mice were treated with acrolein (0.5 μg/kg; i.p.; 7 days) with/without rosiglitazone (Roz: PPARγ ligand, 10 mg/kg; orally; 10 days). Age/weight matched wild type (WT) were used as control. Urine and kidney tissue was processed for western blot and biochemical analysis. KO mice had higher (87%) 8‐Isoprostane in response to acrolein but lower NO (23%) compared to WT. Roz reduced 8‐isoprostane in KO mice by 47% and increased NO production by 35%. Total Antioxidant Status (TAS) was reduced in WT (31%) and KO (53%) mice treated with acrolein. Roz improved TAS in both KO (33%) and WT (21%) mice. Acrolein did not affect expression of eNOS (endothelial nitric oxide synthase) in WT and KO mice. Roz increased eNOS expression in WT (23%) and KO (33%) and iNOS expression in WT (28%). Expression of PPARγ was increased in WT (30%) and KO (28%) mice treated with acrolein and was further increased with addition of Roz (89% & 102% respectively). Based on these data, we are concluding that acrolein‐mediated reduction in PPARγ affects eNOS/iNOS/NO pathway and activation of PPARγ minimizes acrolein effect by improving PPARγ‐dependent NO/NOS interaction. Support or Funding Information NIH, SC3GM103746 This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".